Neural Network Dropout for Geophysical Uncertainty Analysis
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Solution Overview
Problem
Machine learning (ML) methods in exploration geophysics lack reliability and interpretability due to their 'black box' nature, particularly in quantifying epistemic uncertainty in neural network models like CNNs, which are not addressed by existing frameworks such as MCMC, GLUE, or PCM.
Innovation Solution
Implementing a Bayesian approach with Monte Carlo (MC) dropout in neural networks to quantify epistemic uncertainty by randomly setting node outputs to zero during training and inference, generating multiple Monte Carlo samples to approximate the posterior distribution of model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are used for geophysical structure reconstruction, then productivity and automation are improved, but reliability and interpretability deteriorate due to the black box nature of the model
Solution Approach 1:
The patent implements a feedback mechanism by using uncertainty metrics to guide the interpretation process. The system generates uncertainty estimates for each prediction and feeds this information back to the user, allowing them to assess the reliability of each prediction. This creates a closed-loop system where the model's confidence levels inform subsequent decision-making, thereby improving overall reliability without sacrificing productivity.
Solution Approach 2:
The patent introduces uncertainty metrics as an intermediary between the neural network's black box predictions and the user's interpretation. These metrics serve as a mediator that translates the internal model states into meaningful reliability indicators, allowing users to interpret predictions without needing to understand the complex internal workings of the neural network, thus maintaining both productivity and interpretability.
2Reliability
If traditional uncertainty frameworks (MCMC, GLUE, PCM) are applied to geophysical modeling, then reliability is improved through quantitative uncertainty estimation, but device complexity and computational requirements worsen
Solution Approach 1:
The patent extracts the uncertainty quantification capability directly from the neural network by implementing dropout during inference. Instead of applying complex external frameworks like MCMC or GLUE, the method extracts uncertainty information by running the existing neural network multiple times with stochastic dropout enabled, thereby obtaining uncertainty estimates without introducing additional computational frameworks or increasing device complexity.
Solution Approach 2:
The patent changes the operational parameters of the neural network by enabling dropout during the inference phase. This parameter change allows the deterministic neural network to produce stochastic predictions that can be used to estimate uncertainty. By simply adjusting the dropout parameter from 0 (during training) to a small positive value (during inference), the system achieves uncertainty quantification without adding computational complexity.
3Ease of operation
If softmax output is used as probability estimate, then ease of operation is improved by providing continuous output scaled to [0,1], but measurement precision deteriorates as it lacks statistical meaning
Solution Approach 1:
The patent applies partial action by running the neural network multiple times with dropout enabled during inference, rather than a single deterministic pass. This partial repetition (typically 10-100 samples) provides enough statistical information to estimate uncertainty while avoiding excessive computational burden. The mean of these partial samples gives the probability estimate, and the variance provides the uncertainty measure, thus maintaining ease of operation while improving measurement precision.
Data Source
AI summary
A method includes receiving geophysical data representative of a geophysical structure; providing the geophysical data as one or more input data to a neural network; training the neural network to reconstruct the geophysical structure that was received and provide one or more uncertainty metrics for one or more features of the geophysical structure that is reconstructed; reconstructing, using the neural network that has been trained, the geophysical structure; and determining, using the neural network that has been trained, the one or more uncertainty metrics by implementing a second drop out condition on the one or more nodes of the one or more hidden layers of the neural network. The training is performed at least partially by implementing a first drop out condition on one or more nodes of one or more hidden layers of the neural network to randomly set an output of the one or more nodes to zero.